4 VCs illustrate why there’s good reason to be optimistic about the machine learning startup market
When you talk about investments in artificial intelligence startups versus machine learning startups, it’s important to distinguish “AI” from “machine learning.” Those phrases are often used interchangeably, but they carry a slightly different meaning.
Machine learning, or ML, is a method of training AI models so that they can learn to make decisions. Put another way, ML involves training models to solve specific tasks by learning from data and making predictions. AI, on the other hand, is the broader concept for systems that mimic human cognition.
So ML is a subfield of AI but not the same thing.
Lonne Jaffe, managing director at Insight Partners, explains that Insight uses a “three-layer” framework to unpack the definition of an ML startup.
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At the first layer, he says, are core infrastructure companies — products with which a person builds an ML system. At the second layer are apps that seek to tackle a particular use case or workflow using ML. The third layer, meanwhile, comprises ML startups that manifest within an industry as an “actual player” in that industry — think startups that become a startup bank, even if the core of the startup is still ML talent.
According to this framework, examples of ML startups range from Weights & Biases, which provides tools to create and monitor AI models, to Iterative Health, a healthcare company that leverages an ML system designed to identify cancerous polyps from a colonoscopy.
The market for ML is quite large, with a report from Grand View Research estimating that it was worth $49.6 billion in 2022 and could grow at a CAGR of 33.5% by 2030. And it’s been building for some time: A 2021 survey by Dresner Advisory Services found that 59% of all large enterprises are deploying ML, with 50% of those organizations claiming to have 25 or more ML models in use today.
Why is this area growing so fast? 451 Research, the tech R&D group within S&P, posited in a recent report that the initial wave of ML adoption focused on making legacy systems and processes smarter — like business intelligence, customer support, sales and marketing and security. But now, as those applications mature, the attention has shifted to more niche, industry-specific and lucrative ML applications, particularly in finance, retail, manufacturing and healthcare.
Jerry Chen, a partner at Greylock, believes we’re just starting to see what the next generation of ML companies will be. “The cycle is going strong,” he told TechCrunch+. “I’m curious to see how incumbent companies and tech players enter, compete or partner with the startups. In particular, I think we will see some interesting go-to-market partnerships in the next few months.”
But what about the broader VC ecosystem? Are VCs in general optimistic about the future of ML?
To get a better sense, TechCrunch+ surveyed investors including Chen and Jaffe about the state of ML investing today. We touched on the health of the ML funding landscape, and whether the hype around ML, which several years ago was quite strong, is beginning to die down. We also asked investors what challenges stand in the way of ML tech adoption and what the next few months might look like in terms of market growth.
We spoke with:
(Editor’s note: The following responses have been edited for length and clarity.)
Lonne Jaffe, managing director, Insight Partners
How strong is the ML venture fundraising market today and how has it evolved thus far in 2023?
The release of ChatGPT five months ago sparked the fire of startup innovation around ML, along with a renewed fundraising dynamic. We’ve gone from systems of prediction — like classification or recommendation systems — to systems of creation. While funding has been flowing into generative ML systems, there has also been a lot of progress in more “traditional,” discriminative ML systems, like prediction or classification systems.
We’ve been particularly active recently in applied computer vision ML systems in healthcare, some of which may soon match or even exceed human physician performance across certain domains. For example, dental startup Overjet uses AI to analyze dental X-rays to help dentists decide if a tooth needs a filling or a crown, improving patient outcomes.
